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Prediction of flooding in the downstream of the Three Gorges Reservoir based on a back propagation neural network optimized using the AdaBoost algorithm

Author

Listed:
  • Biao Xiong

    (College of Hydraulic and Environmental Engineering, China Three Gorges University
    China Three Gorges University
    China Three Gorges University)

  • Ruiping Li

    (College of Hydraulic and Environmental Engineering, China Three Gorges University
    China Three Gorges University)

  • Dong Ren

    (China Three Gorges University
    China Three Gorges University)

  • Huigang Liu

    (China Three Gorges University
    China Three Gorges University)

  • Tao Xu

    (China Three Gorges University
    China Three Gorges University)

  • Yingping Huang

    (College of Hydraulic and Environmental Engineering, China Three Gorges University
    China Three Gorges University
    China Three Gorges University)

Abstract

Flooding is a natural disaster that threatens people’s lives and causes economic losses. The accurate prediction of water level is of great significance for flood prevention. This study aimed to predict water levels in Wuhan City, which is located in the downstream of the Three Gorges Reservoir Region. In order to improve the accuracy of flood prediction, the AdaBoost algorithm was used to optimize a traditional back propagation neural network (BPNN) in order to resolve the slow convergence speed and local minimum in water level prediction. The improved BPNN was then employed to predict the water level in the study area for prediction intervals of 1 h, 3 h, and 5 h, respectively. Compared with the original BPNN, a generalized regression neural network, and a combination of a genetic algorithm and the original BPNN, the improved BPNN achieved superior water-level prediction. Additionally, the performance of the constructed model was evaluated using the mean absolute error, root-mean-square error (RMSE), mean absolute percentage error (MAPE), the correlation coefficients between the predicted and actual values of water level, and the frequency histograms of the prediction error. The results indicate that the improved BPNN model had a lower prediction error and show a reasonable normal distribution. Therefore, it is concluded that this model is suitable for the prediction of water level.

Suggested Citation

  • Biao Xiong & Ruiping Li & Dong Ren & Huigang Liu & Tao Xu & Yingping Huang, 2021. "Prediction of flooding in the downstream of the Three Gorges Reservoir based on a back propagation neural network optimized using the AdaBoost algorithm," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(2), pages 1559-1575, June.
    • Handle: RePEc:spr:nathaz:v:107:y:2021:i:2:d:10.1007_s11069-021-04646-4
    DOI: 10.1007/s11069-021-04646-4
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    References listed on IDEAS

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    2. Leng, Chunyang & Jia, Mingxing & Zheng, Haijin & Deng, Jibin & Niu, Dapeng, 2023. "Dynamic liquid level prediction in oil wells during oil extraction based on WOA-AM-LSTM-ANN model using dynamic and static information," Energy, Elsevier, vol. 282(C).

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